Machine Learning
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Join the OpenCities AI Challenge and Detect Building Footprints from Aerial Imagery
We partnered with Driven Data and the World Bank to develop the Open Cities AI Challenge. This challenge uses machine learning to extract building footprints in unmapped areas to promote disaster resilience.
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Join the OpenCities AI Challenge and Detect Building Footprints from Aerial Imagery
We partnered with Driven Data and the World Bank to develop the Open Cities AI Challenge. This challenge uses machine learning to extract building footprints in unmapped areas to promote disaster resilience.
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How to Find the Most Recent Satellite Imagery Anywhere on Earth
Every project with satellite imagery begins with a data source. Here’s a rundown of the best options, considering cost, usability, and resolution.
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3 Ways to Analyze the Results of a Supervised Machine Learning Model
How accurate are our supervised machine learning models and what are they really doing? We offer 3 tips to help you better understand these models.
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Ethical Machine Learning for Disaster Relief: Rage for Machine Learning
ML’s predictive powers are driving a rage for deep learning in the crisis management and disaster relief industries. How can these powers be harnessed for ethical machine learning?
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Ethical Machine Learning for Disaster Relief: Avoiding the Second Disaster
Machine learning on satellite imagery is revolutionizing disaster relief. What does ethical machine learning mean in this field?
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Betting Big on the Spatiotemporal Asset Catalog (STAC) Standard
We’re investing heavily in the STAC specification – including building a STAC-compatible Python library and server as well contributing to the Label Extension. We’re hoping this work will help accelerate adoption across the geospatial engineering community more broadly.
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An Introduction to Satellite Imagery and Machine Learning
Today, the availability of satellite imagery still far outpaces our capacity to analyze it, but machine learning and tools like Raster Vision are helping.
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Is This a Crosswalk?: 5 Takeaways from Training a Data Labeling Team
We trained remote computer vision workers to provide data labeling for machine learning projects. Here’s what we learned.
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Transfer Learning from RGB to Multi-band Imagery
We used transfer learning to teach a model to take advantage of multi-band imagery without discarding the original RGB pre-training. This resulted in significant performance improvement.
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Using the Dedupe Machine Learning Library for Cleaning and Matching Data
To deal with issues of apparel facility list data quality and scale quickly and efficiently we need a machine learning tool that can capture the knowledge of domain experts, find commonalities in jumbled text, and confidently compare large lists without the need to compare each individual entry.
